Rage Bait Content on Facebook: Mapping the Perceived Impacts and Engagement Behavior of Students at Visayas State University

1 Descriptive

1.1 Degree

## need cleaning
socio_demo_dta |> 
  mutate(degree = str_remove_all(degree, "[0-9]|-")) |>
  mutate(degree = str_trim(degree, side = "both")) |> 
  count(degree, sort = T) |> 
  View()

1.2 Social Media

1.2.1 Platform

• Female students are more active across diverse platforms, while male students concentrate usage on fewer platforms.

• Females reported higher usage across nearly all platforms, with Facebook, TikTok, and Instagram as their top three.

• Males also favored Facebook, but their engagement was lower overall, with Instagram and TikTok following.

## subtitle
plt_subtitle <- str_wrap("Female students reported higher usage across most platforms, with Facebook, TikTok, and Instagram as the top three; male students also favored Facebook but showed comparatively lower engagement overall.", 120)

## plotting
plt_soc_med <- 
  soc_med_dta |>
  count(sex, social_media) |>
  group_by(sex) |>
  mutate(
    pct = n / sum(n),
    pct_lab = str_c("n=", n),
    social_media = reorder_within(social_media, n, sex)
  ) |>
  ggplot(aes(n, social_media, fill = sex)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = pct_lab), hjust = -0.1) +
  scale_y_reordered() +
  scale_x_continuous(limits = c(0, 80)) +
  facet_wrap(~ sex, scales = "free_y") +
  custom_theme +
  labs(
    title = "Which social media platforms do you use most often?",
    subtitle = plt_subtitle,
    fill = element_blank(),
    y = element_blank()
  )

## saving data
ggsave(
  plot = plt_soc_med,
  filename = "plot/soc_med_gender.jpg",
  unit = "in",
  width = 10,
  height = 7
)

## display plot
knitr::include_graphics("plot/soc_med_gender.jpg")

1.2.2 Social media content engagement by gender

• Gender differences highlight that females lean toward lifestyle and advocacy, while males gravitate more toward gaming and politics.

• Females engaged most with entertainment, music, and lifestyle content, followed by news and educational posts.

• Males showed similar interest in entertainment and music but had stronger engagement with gaming content compared to females.

## plot subtitle
plt_subtitle <- str_wrap("Entertainment, music, and lifestyle content were the most engaged categories among female students, while males showed stronger engagement with entertainment, music, and gaming; overall, females reported higher engagement across most content types.", 140)

plt_soc_med_content_gender <- 
  soc_med_content_dta |> 
  # filter(!str_detect( social_media, "All of it")) |> 
  count(sex, social_media_content) |> 
  group_by(sex) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_content = reorder_within(social_media_content, pct, sex)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_content, fill = sex)) + 
  geom_col(width = 0.8) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 0.3), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ sex, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "What kinds of content do you usually engage with on social media?",
    subtitle = plt_subtitle,
    x = element_blank(),
    y = element_blank(),
    fill = element_blank()
  )

  ## saving plot
  ggsave(
    plot = plt_soc_med_content_gender,
    filename = "plot/soc_med_content_gender.jpg",
    unit = "in",
    height = 6,
    width = 12,
    dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_content_gender.jpg")

1.2.3 Social media content engagement by platform

• Regardless of platform, students prioritize light, entertaining, and lifestyle-driven content, with serious topics less dominant.

• Across platforms (Facebook, Instagram, TikTok, YouTube), entertainment, music, and lifestyle consistently ranked highest.

• Educational and news content also attracted significant engagement, while politics and gaming were lower overall.

## subtitle
plt_subtitle <- str_wrap("Across platforms, entertainment, music, and lifestyle content consistently drew the highest engagement, with Facebook, Instagram, TikTok, and YouTube showing similar patterns; educational and news content also ranked strongly, while politics and gaming remained lower overall.", 120)

plt_soc_med_content <- 
  soc_med_content_dta |> 
  filter(!str_detect( social_media, "All of it")) |> 
  count(social_media, social_media_content) |> 
  group_by(social_media) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_content = reorder_within(social_media_content, pct, social_media)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_content)) + 
  geom_col(width = 0.8) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 0.3), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ social_media, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "What kinds of content do you usually engage with on social media?",
    subtitle = plt_subtitle,
    x = element_blank(),
    y = element_blank()
  )

  ## saving plot
  ggsave(
    plot = plt_soc_med_content,
    filename = "plot/soc_med_content.jpg",
    unit = "in",
    height = 14,
    width = 12,
    dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_content.jpg")

1.2.4 Daily time spent on social media by platform

• Heavy usage patterns suggest social media is deeply embedded in daily routines, with some platforms fostering extended engagement.

• Majority of students spend 3–6 hours daily on Facebook, Instagram, TikTok, and YouTube.

• A notable proportion also reported more than 6 hours, especially on platforms like Reddit, X (Twitter), and Tumblr.

# plot subtitle
plt_subtitle <- str_wrap("Most students reported spending 3–6 hours daily on social media, with Facebook, Instagram, TikTok, and YouTube showing the highest sustained use; a notable share of users also exceeded 6 hours, highlighting heavy engagement across platforms.", 120)

## plottign socila media hours use
plt_soc_med_hours <- 
  soc_med_category |> 
  filter(!str_detect( social_media, "All of|Tele")) |> 
  count(social_media, social_media_hours) |> 
  group_by(social_media) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_hours = reorder_within(social_media_hours, pct, social_media)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_hours)) + 
  geom_col(width = 0.8, position = position_dodge2(preserve = "single")) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 1), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ social_media, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = "On average, how many hours per day do you spend on social media?",
    x = NULL,
    y = NULL,
    subtitle = plt_subtitle
  )


## saving plot
ggsave(
  plot = plt_soc_med_hours,
  filename = "plot/soc_med_hours.jpg",
  unit = "in",
  height = 10,
  width = 10,
  dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_hours.jpg")

1.2.5 Daily time spent on social media by platform

• Male students demonstrate more extreme usage, while female students cluster around moderate-to-high daily engagement

• Females most often reported 3–6 hours daily, with fewer exceeding 6 hours.

• Males were more likely to report over 6 hours of use, showing heavier overall consumption.

# plot subtitle
plt_subtitle <- str_wrap("Female students most often reported spending 3–6 hours daily on social media, while males were more likely to exceed 6 hours; overall, both groups showed heavy usage concentrated in the mid-to-high ranges.", 100)

## plottign socila media hours use
plt_soc_med_hours_gender <- 
  soc_med_category |> 
  count(sex, social_media_hours) |> 
  group_by(sex) |> 
  mutate(pct = n / sum(n)) |> 
  mutate(social_media_hours = reorder_within(social_media_hours, pct, sex)) |> 
  mutate(pct_label = str_c("n=", n, " (", round(pct*100, ), "%)")) |> 
  ggplot(aes(pct, social_media_hours, fill = sex)) + 
  geom_col(width = 0.6, position = position_dodge2(preserve = "single")) +
  geom_text(aes(label = pct_label), hjust = -0.1) +
  scale_x_continuous(limits = c(0, 1), labels = scales::percent_format()) +
  scale_y_reordered() +
  facet_wrap(~ sex, scales = "free", ncol = 2) +
  custom_theme +
  labs(
    title = str_wrap("On average, how many hours per day do you spend on social media?", 60),
    x = NULL,
    y = NULL,
    fill = NULL,
    subtitle = plt_subtitle
  )


## saving plot
ggsave(
  plot = plt_soc_med_hours_gender,
  filename = "plot/soc_med_hours_gender.jpg",
  unit = "in",
  height = 6,
  width = 8,
  dpi = 400
  )

## display plot
knitr::include_graphics("plot/soc_med_hours_gender.jpg")